Stream Loyalty
Introducing AI to a close-knit development team with care: start with their real workflow pain points, prove value in a familiar toolset, and build the context needed to work responsibly with a complex legacy codebase.
The Challenge
Stream Loyalty had a three-developer team with a close-knit, family-like culture. The opportunity was to bring AI into their development work without disrupting what already worked or asking the developers to accept a sweeping process change.
Their established codebase also came with legacy technology and significant complexity. Any AI approach needed to give the tools useful project context while being mindful of the cost of sending that context.
A People-First AI Approach
Assess before changing
Reviewed the development lifecycle and day-to-day workflows, identifying three key areas for improvement before recommending changes.
Find practical AI opportunities
Worked with the developers to identify four areas where AI could support their existing workflows, then created a proof of concept.
Set up the right tools
Got the team hands-on with Microsoft Copilot, a natural fit for their Azure environment, and trained developers to code effectively with AI.
Build context for a legacy stack
Worked on a knowledge base to help AI work with the complexity of the existing codebase while keeping unnecessary context—and associated AI costs—in check.
A Steady, Hands-On Engagement
The Result: A Practical Starting Point
By the end of the four-month engagement, the team had an AI proof of concept grounded in its own workflows, developers set up and trained with Microsoft Copilot, and knowledge-base work underway to address the realities of its legacy codebase.
Most importantly, the work focused on relieving the team's biggest pain points—not replacing their working habits—so they could explore AI with useful context, cost awareness, and their team culture intact.